diff --git a/code/base_chirps.py b/code/base_chirps.py index 5ae38ed..e8064a2 100644 --- a/code/base_chirps.py +++ b/code/base_chirps.py @@ -1,6 +1,6 @@ from read_chirp_data import * +from func_chirp import * from utility import * -#import nix_helpers as nh import matplotlib.pyplot as plt import numpy as np from IPython import embed @@ -18,45 +18,13 @@ eod = read_chirp_eod(os.path.join(data_dir, dataset)) times = read_chirp_times(os.path.join(data_dir, dataset)) df_map = map_keys(eod) +chirp_eod_plot(df_map, eod, times) +plt.close() -#die äußere Schleife geht für alle Keys durch und somit durch alle dfs -#die innnere Schleife bildet die 16 Wiederholungen einer Frequenz ab -for i in df_map.keys(): - freq = list(df_map[i]) - fig,axs = plt.subplots(2, 2, sharex = True, sharey = True) - - for idx, k in enumerate(freq): - ct = times[k] - e1 = eod[k] - zeit = e1[0] - eods = e1[1] - - if idx <= 3: - axs[0, 0].plot(zeit, eods, color= 'blue', linewidth = 0.25) - axs[0, 0].scatter(np.asarray(ct), np.ones(len(ct))*3, color = 'green', s= 22) - elif 4<= idx <= 7: - axs[0, 1].plot(zeit, eods, color= 'blue', linewidth = 0.25) - axs[0, 1].scatter(np.asarray(ct), np.ones(len(ct))*3, color = 'green', s= 22) - elif 8<= idx <= 11: - axs[1, 0].plot(zeit, eods, color= 'blue', linewidth = 0.25) - axs[1, 0].scatter(np.asarray(ct), np.ones(len(ct))*3, color = 'green', s= 22) - else: - axs[1, 1].plot(zeit, eods, color= 'blue', linewidth = 0.25) - axs[1, 1].scatter(np.asarray(ct), np.ones(len(ct))*3, color = 'green', s= 22) - - -fig.suptitle('EOD for chirps', fontsize = 16) -axs[0,0].set_ylabel('Amplitude [mV]') -axs[0,1].set_xlabel('Amplitude [mV]') -axs[1,0].set_xlabel('Time [ms]') -axs[1,1].set_xlabel('Time [ms]') - - - -#for i in df_map.keys(): - -freq = list(df_map[-50]) +#ACHTUNG: df für beide Plots anpassen! +#momentan per Hand durch alle Frequenzen +freq = list(df_map[-100]) ls_mod = [] ls_beat = [] for k in freq: @@ -78,11 +46,11 @@ beat_mod = np.std(ls_beat) #Std vom Bereich vor dem Chirp plt.figure() plt.scatter(np.arange(0,len(ls_mod),1), ls_mod) plt.scatter(np.arange(0,len(ls_mod),1), np.ones(len(ls_mod))*beat_mod, color = 'violet') -plt.show() +plt.close() -#Chirps einer Phase zuordnen - zusammen plotten? +#Chirps einer Phase zuordnen - zusammen plotten dct_phase = {} chirp_spikes = read_chirp_spikes(os.path.join(data_dir, dataset)) @@ -98,8 +66,8 @@ for i in sort_df: for k in freq: for phase in chirp_spikes[k]: dct_phase[i].append(phase[1]) - #for idx in np.arange(num_bin): - #if phase[1] > phase_vec[idx] and phase[1] < phase_vec[idx+1]: - -print(dct_phase) +plt.figure() +plt.scatter(dct_phase[-100], ls_mod) +plt.title('Change of std depending on the phase where the chirp occured') +plt.show() diff --git a/code/base_spikes.py b/code/base_spikes.py index 90525a6..a6c4720 100644 --- a/code/base_spikes.py +++ b/code/base_spikes.py @@ -9,20 +9,19 @@ from IPython import embed #Funktionen imposrtieren data_dir = "../data" dataset = "2018-11-13-ad-invivo-1" -#data = ("2018-11-09-ad-invivo-1", "2018-11-13-aa-invivo-1", "2018-11-13-ad-invivo-1", "2018-11-09-af-invivo-1", "2018-11-09-ag-invivo-1", "2018-11-13-ah-invivo-1", "2018-11-13-ai-invivo-1", "2018-11-13-aj-invivo-1", "2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1", "2018-11-14-aa-invivo-1", "2018-11-14-ab-invivo-1", "2018-11-14-ac-invivo-1", "2018-11-14-ad-invivo-1", "2018-11-14-ae-invivo-1", "2018-11-14-af-invivo-1", "2018-11-14-ag-invivo-1", "2018-11-14-ah-invivo-1", "2018-11-14-aj-invivo-1", "2018-11-14-ak-invivo-1", "2018-11-14-al-invivo-1", "2018-11-14-am-invivo-1", "2018-11-14-an-invivo-1", "2018-11-20-aa-invivo-1", "2018-11-20-ab-invivo-1", "2018-11-20-ac-invivo-1", "2018-11-20-ad-invivo-1"," 2018-11-20-ae-invivo-1", "2018-11-20-af-invivo-1", "2018-11-20-ag-invivo-1", "2018-11-20-ah-invivo-1", "2018-11-20-ai-invivo-1") +#data = ("2018-11-09-ad-invivo-1", "2018-11-13-aa-invivo-1", "2018-11-13-ad-invivo-1", "2018-11-09-af-invivo-1", "2018-11-09-ag-invivo-1", "2018-11-13-ah-invivo-1", "2018-11-13-ai-invivo-1", "2018-11-13-aj-invivo-1", "2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1", "2018-11-14-aa-invivo-1", "2018-11-14-ab-invivo-1", "2018-11-14-ac-invivo-1", "2018-11-14-ad-invivo-1", "2018-11-14-ae-invivo-1", "2018-11-14-af-invivo-1", "2018-11-14-ag-invivo-1", "2018-11-14-ah-invivo-1", "2018-11-14-aj-invivo-1", "2018-11-14-ak-invivo-1", "2018-11-14-al-invivo-1", "2018-11-14-am-invivo-1", "2018-11-14-an-invivo-1", "2018-11-20-aa-invivo-1", "2018-11-20-ab-invivo-1", "2018-11-20-ac-invivo-1", "2018-11-20-ad-invivo-1"," 2018-11-20-ae-invivo-1", "2018-11-20-af-invivo-1", "2018-11-20-ag-invivo-1", "2018-11-20-ah-invivo-1", "2018-11-20-ai-invivo-1") Durchgang für alle Datensets - zwischenspeichern von Daten? spike_times = read_baseline_spikes(os.path.join(data_dir, dataset)) -#inst_frequency = 1. / np.diff(spike_times) -spike_rate = np.diff(spike_times) +spike_iv = np.diff(spike_times) x = np.arange(0.001, 0.01, 0.0001) -plt.hist(spike_rate,x) +plt.hist(spike_iv,x) -mu = np.mean(spike_rate) -sigma = np.std(spike_rate) +mu = np.mean(iv) +sigma = np.std(iv) cv = sigma/mu plt.title('A.lepto ISI Histogramm', fontsize = 14) @@ -45,20 +44,28 @@ sort_df = sorted(df_map.keys()) plt.figure() dct_rate = {} +overall_r = {} for i in sort_df: freq = list(df_map[i]) dct_rate[i] = [] + overall_r[i] = [] for k in freq: for phase in chirp_spikes[k]: spikes = chirp_spikes[k][phase] rate = len(spikes)/ 1.2 dct_rate[i].append(rate) + #overall_r[i].extend(rate) #kann man nicht erweitern! - -for h in sort_df: +ls_mean = [] +for h in sort_df: + mean = np.mean(dct_rate[h]) + ls_mean.append(mean) plt.plot(np.arange(0,len(dct_rate[h]),1),dct_rate[h], label = h) #plt.vlines(10, ymin = 190, ymax = 310) +#Anfang Spur und Endpunkt bestimmen +#relativ zur mittleren Feuerrate +#wie hoch ist die Adaption von Zellen plt.legend() plt.title('Firing rate of the cell for all trials, sorted by df') plt.xlabel('# of trials') @@ -66,18 +73,23 @@ plt.ylabel('Instant firing rate of the cell') plt.show() -#mittlere Feuerrate einer Frequenz auf Frequenz + +#mittlere Feuerrate einer Frequenz auf Frequenz: plt.figure() -ls_mean = [] -for d in sort_df: - mean = np.mean(dct_rate[d]) - ls_mean.append(mean) plt.plot(np.arange(0,len(ls_mean),1),ls_mean) +#plt.scatter(np.arange(0,len(ls_mean),1), np.mean(int(overall_r))) plt.title('Mean firing rate of a cell for a range of frequency differences') -plt. xticks(np.arange(len(sort_df)), (sort_df)) +plt. xticks(np.arange(1,len(sort_df),1), (sort_df)) plt.xlabel('Range of frequency differences [Hz]') plt.ylabel('Mean firing rate of the cell') plt.show() + +#Boxplot +#wie viel Prozent macht die Adaption von Zellen aus? + + +#Reihen-Plot +#macht die zeitliche Reihenfolge der Präsentation einen Unterschied in der Zellantwort?